This solution will work for axes level plots produced with matplotlib, seaborn, and pandas.DataFrame.plot. The main idea would be to separate the problem into small pieces: Get the flag as an array into the script. E.g. def get_flag(name): path = “path/to/flag/{}.png”.format(name) im = plt.imread(path) return im Position an image at a certain position in a … Read more
A brace / bracket can be plotted direct with matplotlib.pyplot.plot or matplotlib.axes.Axes.plot, and annotations can be added with matplotlib.pyplot.text or matplotlib.axes.Axes.text. seaborn categorical plots are 0 indexed, whereas box plots, by default, with matplotlib and pandas, start at range(1, N+1), which can be adjusted with the positions parameter. seaborn is a high-level API for matplotlib, … Read more
Aside from cleaning up your data into a tidy format, you need to reformat the text data (percentages) into numeric data types. Since that has nothing to do with barplots, I’ll assume you can take care of that on your own and focus on the plotting and data structures instead: df = pandas.DataFrame({ ‘Factor’: [‘Growth’, … Read more
You can do this by making a twinx axes for the frequencies. You can switch the two y axes around so the frequencies stay on the left and the counts on the right, but without having to recalculate the counts axis (here we use tick_left() and tick_right() to move the ticks and set_label_position to move … Read more
I think need melt if want use barplot: data = df.melt(‘date’, var_name=”a”, value_name=”b”) print (data) date a b 0 2017-09-05 A 25 1 2017-09-06 A 261 2 2017-09-07 A 188 3 2017-09-08 A 200 4 2017-09-09 A 292 5 2017-09-05 B 261 6 2017-09-06 B 1519 7 2017-09-07 B 1545 8 2017-09-08 B 2110 9 … Read more
I had a problem with the answer by @mwaskorn, namely that g.set_xticklabels(rotation=30) fails, because this also requires the labels. A bit easier than the answer by @Aman is to just add plt.xticks(rotation=45)
Imports and Data import numpy import pandas import matplotlib.pyplot as plt import seaborn as sns seaborn.set(style=”ticks”) numpy.random.seed(0) N = 37 _genders= [‘Female’, ‘Male’, ‘Non-binary’, ‘No Response’] df = pandas.DataFrame({ ‘Height (cm)’: numpy.random.uniform(low=130, high=200, size=N), ‘Weight (kg)’: numpy.random.uniform(low=30, high=100, size=N), ‘Gender’: numpy.random.choice(_genders, size=N) }) Update August 2021 With seaborn 0.11.0, it’s recommended to use new figure … Read more
Imports and Sample DataFrame import matplotlib.pyplot as plt import pandas as pd import seaborn as sns # for sample data from matplotlib.lines import Line2D # for legend handle # DataFrame used for all options df = sns.load_dataset(‘diamonds’) carat cut color clarity depth table price x y z 0 0.23 Ideal E SI2 61.5 55.0 326 … Read more
Unfortunately matplotlib 3.1.1 broke seaborn heatmaps; and in general inverted axes with fixed ticks. This is fixed in the current development version; you may hence revert to matplotlib 3.1.0 use matplotlib 3.1.2 or higher set the heatmap limits manually (ax.set_ylim(bottom, top) # set the ylim to bottom, top)
I think you want units in the call to relplot and then add a layer of lineplot using map: import seaborn as sns import pandas as pd fm = sns.load_dataset(‘fmri’).query(“event == ‘stim'”) g = sns.relplot( data=fm, kind=’line’, col=”region”, x=’timepoint’, y=’signal’, units=”subject”, estimator=None, color=”.7″ ) g.data = fm # Hack needed to work around bug on … Read more